
import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_    :			List[str]    =	logging.get_logger(__name__)

lowerCamelCase_    :			int    =	[
    ["""attention""", """attn"""],
    ["""encoder_attention""", """encoder_attn"""],
    ["""q_lin""", """q_proj"""],
    ["""k_lin""", """k_proj"""],
    ["""v_lin""", """v_proj"""],
    ["""out_lin""", """out_proj"""],
    ["""norm_embeddings""", """layernorm_embedding"""],
    ["""position_embeddings""", """embed_positions"""],
    ["""embeddings""", """embed_tokens"""],
    ["""ffn.lin""", """fc"""],
]







def 							A__       (    lowerCamelCase    )	->						Optional[Any]:
				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								UpperCamelCase_:				Dict						=    k.replace(lowerCamelCase  ,     lowerCamelCase    )

				if k.startswith("""encoder"""    ):
								UpperCamelCase_:				List[Any]						=    k.replace(""".attn"""  ,     """.self_attn"""    )
								UpperCamelCase_:				str						=    k.replace("""norm1"""  ,     """self_attn_layer_norm"""    )
								UpperCamelCase_:				List[str]						=    k.replace("""norm2"""  ,     """final_layer_norm"""    )
				elif k.startswith("""decoder"""    ):
								UpperCamelCase_:				Optional[int]						=    k.replace("""norm1"""  ,     """self_attn_layer_norm"""    )
								UpperCamelCase_:				Union[str, Any]						=    k.replace("""norm2"""  ,     """encoder_attn_layer_norm"""    )
								UpperCamelCase_:				Dict						=    k.replace("""norm3"""  ,     """final_layer_norm"""    )
				return k







def 							A__       (    lowerCamelCase    )	->						Optional[Any]:
				UpperCamelCase_:				List[Any]						=    [
				    """model.encoder.layernorm_embedding.weight""",
				    """model.encoder.layernorm_embedding.bias""",
				    """model.decoder.layernorm_embedding.weight""",
				    """model.decoder.layernorm_embedding.bias""",
				]
				for k in keys:
								UpperCamelCase_:				int						=    sd.pop(lowerCamelCase    )
								UpperCamelCase_:				List[str]						=    k.replace("""layernorm_embedding"""  ,     """layer_norm"""    )
								assert new_k not in sd
								UpperCamelCase_:				Optional[Any]						=    v


lowerCamelCase_    :			str    =	["""START"""]







@torch.no_grad()
def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Tuple:
				UpperCamelCase_:				Optional[int]						=    torch.load(lowerCamelCase  ,     map_location="""cpu"""    )
				UpperCamelCase_:				List[str]						=    model["""model"""]
				UpperCamelCase_:				Tuple						=    BlenderbotConfig.from_json_file(lowerCamelCase    )
				UpperCamelCase_:				Union[str, Any]						=    BlenderbotForConditionalGeneration(lowerCamelCase    )
				UpperCamelCase_:				str						=    m.model.state_dict().keys()
				UpperCamelCase_:				Any						=    []
				UpperCamelCase_:				List[Any]						=    {}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

								UpperCamelCase_:				int						=    rename_state_dict_key(lowerCamelCase    )
								if new_k not in valid_keys:
												failures.append([k, new_k]    )
								else:
												UpperCamelCase_:				str						=    v
				if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
								rename_layernorm_keys(lowerCamelCase    )
				m.model.load_state_dict(lowerCamelCase  ,     strict=lowerCamelCase    )
				m.half()
				m.save_pretrained(lowerCamelCase    )


if __name__ == "__main__":
							lowerCamelCase_    :			Optional[int]    =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument("""--src_path""", type=str, help="""like blenderbot-model.bin""")
							parser.add_argument("""--save_dir""", default="""hf_blenderbot""", type=str, help="""Where to save converted model.""")
							parser.add_argument(
							    """--hf_config_json""", default="""blenderbot-3b-config.json""", type=str, help="""Path to config to use"""
							)
							lowerCamelCase_    :			Any    =	parser.parse_args()
							convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_    :			Any    =	logging.get_logger(__name__)

lowerCamelCase_    :			Dict    =	{
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Any          =			"""dpr"""

			def __init__(							self			:							str    ,						snake_case_			:							Any=3_0522    ,						snake_case_			:							List[str]=768    ,						snake_case_			:							List[str]=12    ,						snake_case_			:							Union[str, Any]=12    ,						snake_case_			:							Tuple=3072    ,						snake_case_			:							Any="gelu"    ,						snake_case_			:							Optional[Any]=0.1    ,						snake_case_			:							str=0.1    ,						snake_case_			:							Dict=512    ,						snake_case_			:							int=2    ,						snake_case_			:							Union[str, Any]=0.02    ,						snake_case_			:							str=1e-12    ,						snake_case_			:							List[str]=0    ,						snake_case_			:							Any="absolute"    ,						snake_case_			:							int = 0    ,						**snake_case_			:							int    ,						):
							super().__init__(pad_token_id=snake_case_    ,						**snake_case_   )

							UpperCamelCase_:				str						=    vocab_size
							UpperCamelCase_:				Optional[int]						=    hidden_size
							UpperCamelCase_:				int						=    num_hidden_layers
							UpperCamelCase_:				Any						=    num_attention_heads
							UpperCamelCase_:				int						=    hidden_act
							UpperCamelCase_:				Optional[Any]						=    intermediate_size
							UpperCamelCase_:				Tuple						=    hidden_dropout_prob
							UpperCamelCase_:				Tuple						=    attention_probs_dropout_prob
							UpperCamelCase_:				str						=    max_position_embeddings
							UpperCamelCase_:				Any						=    type_vocab_size
							UpperCamelCase_:				Tuple						=    initializer_range
							UpperCamelCase_:				List[Any]						=    layer_norm_eps
							UpperCamelCase_:				Tuple						=    projection_dim
							UpperCamelCase_:				Dict						=    position_embedding_type






